Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior
Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior
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DOI：
10.1109/tits.2016.2603007
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发表时间：
2017-05-01
影响因子：
8.5
通讯作者：
Kochenderfer, Mykel J.
中科院分区：
文献类型：
--
作者：
Morton, Jeremy;Wheeler, Tim A.;Kochenderfer, Mykel J.
任何交通仿真模型的有效性都取决于其生成具有代表性的驾驶员加速度曲线的能力。本文研究了循环神经网络在预测高速公路上跟车加速度分布方面的有效性。长短期记忆循环网络经过训练并用于在 10 秒范围内传播模拟车辆轨迹。基于几个性能指标，循环网络在复制驾驶员行为方面通常匹配或优于基线方法，包括真实轨迹中存在的平滑度和振荡特征。本文揭示了强大的性能归因于循环网络识别自我车辆状态的最新趋势的能力，并且循环网络被证明与具有较长历史作为输入的前馈网络一样表现。
The validity of any traffic simulation model depends on its ability to generate representative driver acceleration profiles. This paper studies the effectiveness of recurrent neural networks in predicting the acceleration distributions for car following on highways. The long short-term memory recurrent networks are trained and used to propagate the simulated vehicle trajectories over 10-s horizons. On the basis of several performance metrics, the recurrent networks are shown to generally match or outperform baseline methods in replicating driver behavior, including smoothness and oscillatory characteristics present in real trajectories. This paper reveals that the strong performance is due to the ability of the recurrent network to identify recent trends in the ego-vehicle's state, and recurrent networks are shown to perform as, well as feedforward networks with longer histories as inputs.